Basic stats
## [1] "number of trees sampled: 398"
Partial regression

##
## Call:
## lm(formula = log(tmp$cmax) ~ log(tmp$bM1c))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.56369 -0.10123 0.00619 0.11219 0.50895
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.300139 0.008888 146.29 <2e-16 ***
## log(tmp$bM1c) 0.228591 0.012743 17.94 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1701 on 374 degrees of freedom
## Multiple R-squared: 0.4625, Adjusted R-squared: 0.4611
## F-statistic: 321.8 on 1 and 374 DF, p-value: < 2.2e-16


##
## Call:
## lm(formula = log(tmp$tL) ~ log(tmp$bM1c))
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9103 -0.4693 0.0759 0.5869 2.0180
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.84072 0.04324 88.81 <2e-16 ***
## log(tmp$bM1c) 0.63125 0.06200 10.18 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8276 on 374 degrees of freedom
## Multiple R-squared: 0.217, Adjusted R-squared: 0.2149
## F-statistic: 103.7 on 1 and 374 DF, p-value: < 2.2e-16


##
## Call:
## lm(formula = lb$residuals ~ hb$residuals)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8943 -0.4751 0.0816 0.5886 2.0014
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.630e-17 4.264e-02 0.000 1.000
## hb$residuals 2.099e-01 2.514e-01 0.835 0.404
##
## Residual standard error: 0.8269 on 374 degrees of freedom
## Multiple R-squared: 0.00186, Adjusted R-squared: -0.0008087
## F-statistic: 0.697 on 1 and 374 DF, p-value: 0.4043
##
## Call:
## lm(formula = log(tmp$rgrBc) ~ log(tmp$tL))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.91203 -0.27562 0.08529 0.37540 0.96666
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.42401 0.10822 -22.399 < 2e-16 ***
## log(tmp$tL) 0.10416 0.02787 3.738 0.000214 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.504 on 374 degrees of freedom
## Multiple R-squared: 0.03602, Adjusted R-squared: 0.03344
## F-statistic: 13.97 on 1 and 374 DF, p-value: 0.0002144


##
## Call:
## lm(formula = gl$residuals ~ lb$residuals)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.91292 -0.27639 0.08496 0.37561 0.96774
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.856e-17 2.599e-02 0.000 1.000
## lb$residuals -9.983e-04 3.149e-02 -0.032 0.975
##
## Residual standard error: 0.504 on 374 degrees of freedom
## Multiple R-squared: 2.687e-06, Adjusted R-squared: -0.002671
## F-statistic: 0.001005 on 1 and 374 DF, p-value: 0.9747